We initialize DNN parameters \(\theta _X\)  and \(\theta _T\)  with two stacked denoising autoencoders. Stacked denoising autoencoders have shown success in generating semantically meaningful representations for both text and images in several studies (c.f., [1]}, [2]}, [3]}). We train the stacked denoising autoencoders to learn the initial DNN parameters for each view by minimizing mean square error reconstruction loss. After training the autoencoders, we discard the decoders, pass data \(x_i\)  and \(t_i\)  through the trained encoders to obtain the initialized embeddings \(z_i\)  and \(z_i^{\prime }\) . Then, we apply K-means to the embeddings \(z_i\)  and \(z_i^{\prime }\)  to obtain initialized centroid sets \(\mu _j\)  and \(\mu ^{\prime }_j\) .
